{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Boston Housing Prices\n",
    "\n",
    "This project was completed as a part of the Machine Learning Engineer Nanodegree from Udacity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In this project, we will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a *good fit* could then be used to make certain predictions about a home's monetary value.\n",
    "\n",
    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Housing). The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. \n",
    "\n",
    "For the purposes of this project, the following preprocessing steps have been made to the dataset:\n",
    "- 16 data points have an `'MEDV'` value of 50.0. These data points likely contain **missing or censored values** and have been removed.\n",
    "- 1 data point has an `'RM'` value of 8.78. This data point can be considered an **outlier** and has been removed.\n",
    "- The features `'RM'`, `'LSTAT'`, `'PTRATIO'`, and `'MEDV'` are essential. The remaining **non-relevant features** have been excluded.\n",
    "- The feature `'MEDV'` has been **multiplicatively scaled** to account for 35 years of market inflation.\n",
    "\n",
    "We'll start with the reading in the data, and separating the features and prices for homes into different pandas dataframes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston housing dataset has 489 data points with 4 variables each.\n"
     ]
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "# Import supplementary visualizations code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the Boston housing dataset\n",
    "data = pd.read_csv('housing.csv')\n",
    "prices = data['MEDV']\n",
    "features = data.drop('MEDV', axis = 1)\n",
    "    \n",
    "# Success\n",
    "print(\"Boston housing dataset has {} data points with {} variables each.\".format(*data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "In this section, we will make a cursory investigation about the Boston housing data.\n",
    "\n",
    "Since the main goal of this project is to construct a working model which has the capability of predicting the value of houses, we have separated the dataset into **features** and the **target variable**. The **features**, `'RM'`, `'LSTAT'`, and `'PTRATIO'`, give us quantitative information about each data point. The **target variable**, `'MEDV'`, will be the variable we seek to predict. These are stored in `features` and `prices`, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Statistics\n",
    "\n",
    "We'll start with calculating some descriptive statistics about the Boston housing prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for Boston housing dataset:\n",
      "\n",
      "Minimum price: $105,000.00\n",
      "Maximum price: $1,024,800.00\n",
      "Mean price: $454,342.94\n",
      "Median price $438,900.00\n",
      "Standard deviation of prices: $165,171.13\n"
     ]
    }
   ],
   "source": [
    "minimum_price = np.min(prices)\n",
    "maximum_price = np.max(prices)\n",
    "mean_price = np.mean(prices)\n",
    "median_price = np.median(prices)\n",
    "std_price = np.std(prices)\n",
    "\n",
    "# Show the calculated statistics\n",
    "print(\"Statistics for Boston housing dataset:\\n\")\n",
    "print(\"Minimum price: ${:,.2f}\".format(minimum_price))\n",
    "print(\"Maximum price: ${:,.2f}\".format(maximum_price))\n",
    "print(\"Mean price: ${:,.2f}\".format(mean_price))\n",
    "print(\"Median price ${:,.2f}\".format(median_price))\n",
    "print(\"Standard deviation of prices: ${:,.2f}\".format(std_price))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Observation\n",
    "\n",
    "To dive a bit deeper int our data, we are using three features from the Boston housing dataset: `'RM'`, `'LSTAT'`, and `'PTRATIO'`. For each data point (neighborhood):\n",
    "- `'RM'` is the average number of rooms among homes in the neighborhood.\n",
    "- `'LSTAT'` is the percentage of homeowners in the neighborhood considered \"lower class\" (working poor).\n",
    "- `'PTRATIO'` is the ratio of students to teachers in primary and secondary schools in the neighborhood.\n",
    "\n",
    "Without building a model, let's try to figure out if an increase in the value of a feature would lead to an **increase** in the value of `'MEDV'` or a **decrease** in the value of `'MEDV'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **'RM':** An increase in the value of this feature will lead to an increase in the value of 'MEDV'. This is because for you'd expect a home with a higher number of rooms to be more expensive that a home with lower number of rooms. \n",
    "- **'LSTAT':** An increase in the value of this feature will lead to a decrease in the value of 'MEDV'. A lower class homeowner might not be able to afford expensive houses, so you'd expect them to leave in a cheaper home. A higher percentage of such people could correlate to cheaper homes in an area, and thus, a lower 'MEDV' value.\n",
    "- **'PTRATIO':** An increase in the value of this feature will lead to an decrease in the value of 'MEDV'. A low student to teacher ration is typically associated with better education level of a school, as a teacher is able to focus on individual students better (than if there were more students). So, due to the presence of better quality schools, people might be willing to pay more to live in those areas, to provide their children with better education, and the prices might be higher.\n",
    "\n",
    "We can build scatterplots to see if our intuition is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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rJLX6MlJuwLquWLGUwnSr9sJn722LcHIkjYU2CZUigCXCieEMIpT17IWFE83d+/r5q+++xZHBFCvbo9y0fWYTzqsnRnjwmcO8dHS4eOzteQG4Yh4CEAnZdMRCJML2oph/ZsIIgcGwSCxUcbNGU6sv4749B3H9ySQxW4RAKe748otcsaGrTDwKn11f02FgojwhSwDHEiwR/HxIatr1F1w0d+/r57N//yqWBW0RHe8/XVG4106O8OAzR3jxyFDx2CWrk9x6zSa2b+ya0+QtIiQiNu3RxTf/zIQRAoNhkWiVFo+1+jIO9I8xknKxLEGgGDZpW0wRj1JT0sCEjhyyBAKlRSDsWHiBws/X7nd9xWhai8VCiWYq53HPdw4gAlFn+qJwb5wa5cFnDvP84UkB2LYqyW3XbOLKTXMTgIL5pz0Wwraau/qvhhECg2ERaYUWj7X6MnJeAKLNOVk/QNC2fqVUVfHYta2Pu5/chyWCbUsx3FQpCIIASwRLLBQKSwQFhG2LkbQ7L9EszQc4OTJ9Ubg3To3y0N4jPHdosPjcRSvbuO2aTVy1uXtOArCUzD8zYYTAYDCUUasvI2QLaVfH3VeLtqwmHgfPTWDlxQMgZFnk/AA3gLCN3h4oYVVHFNsSOuNhvvWp6+b0Ofx8PsBYST7A6vYYAxPZMv/DaNoj7fp8/CsvF49d2NfGbddsZOeWnron8KVq/pkJIwQGg6GMWn0ZF61s59C5ccYyHjkfEJ05GslPfjPZ9D0/wFc6u7iwkwiUImJbrEhGSEZDKKXm7BweSbsMp6bmA9x05XruefoAadcHFGfHc2TcSbf1hSvauPWajVxzQf0C4FgW7TGHZHRpmn9mwgiBwWAoo1ZfRkEwVnU4jKVd+sdzeIDl+ZwbzxCy7Snisbknzv4z41UjhnrbIqxIRovHqgnJbNFM6ZzOB5iuLtCOLd3cOLyOh/YeZjQzGTJ6wYoEt+7cxHsurF8AoiEd+7/UzT8zIa1W0WH79u3qhRdeaPYwDAYDemK++8l97O8fxxY9oftKl0b4+K4L+MT7Lppy/m9/6QVcXyGgdxEiRB1hwg2wKPgZtAO59Bq79/Xz6cde0bkIQVB0wP7Jje/kmgt7Z60L9Fb/OA/uPcwP3hwoHtvSm+CWazZy7YW9RXNVLRTMPx2xEBGnNcw/IvKiUmp7tefMjsBgMFSlllyCXdv6uOtbbxCyC1nBFqvaIji2sPfgIJflr7H/zCiur3ThNAURWwjQzuBE2GYo7YKCQpsXAWIhi8deOsFl6zqLjuahlIttCY5toRQMTuT4z998nftuqTq/AfDW2XEe3nuE7x04Vzy2qSfOrdds4l9srU8AQnYh+av1zD8zYYTAYDBModZcgt37+jlwdhxbJvsMnBxJs6YjyoEzo3zu8dfIeX7RDJPO+fm6RLCuK0YyGmL/6VE8X6HQhesc0fkEhfpDhcijUkezUiqfgAaHB6v7EQ6eHefhZ4+wZ/+kAGzsiXPrzo1cd9GKugQgFtbO30Tk/Jwyz89PZTAsc+qtclp5/nAqV1MuwX17DhKyrPwkPlmO+sxYlrBt0WELA+O6IY1l6YSzQuW50yMZlFJkS3oSKAWuUoQsRc6XqpFHSilmMmgfOjfBw3uP8N39kxXtN3THuWXnRn7mohU1r+QtEdqiDu3RUENLQC8FjBAYDOcZ9VY5rXb+4YEU6zqjZedVm5SPDaVY2R7h1Ei2WIVUofB8CFmK0yMZJnI+loCtdP6Ar2BtZ5TTo1lOj2Z1VnHetFTo6+IFEA9bZQ7jjd0x3jo7gRK9c1BKJ6Rt7IoBcGRAC8Dun54tCsW6rhi37NzI//O2vpoFIGRbtMdCJCNOsZHO+Y4RAoPhPKPeKqel549lXM6OZfEDxdHBNBt79ER4dixLxvNJhB127+svXqeQc7CmM8rZsWyx1MTK9gj9Y1mCvAknUDo8VKGIODaObXHFhi6ODaWwBU6NZLEt8PK7AwUkow6ur/jtazdzdizLb71nC3c/tY+JnEcQKCxLaA+H+ODla/njb77BP+/rLwrA2s4Yv7FzIz+7rXYBSET06j8Wbg3n70JihMBgOM+ot8pp4fyxjMvJfMG3QlG4wwP6NbalTSXxsF22uyitIbS5N1HMOYiHLLoTIQbGXWxLJ3cVVvqWH3B4IKUb2Ecccn5QFBKlAvxA1xra1JPgN67eyJa+NsYyLju2dPPBd67h7148Ttr3CYnQHQ/zZ985UExoW9MZ5Teu3sj7Ll5ZkwDYlpDMN34JNany51LACIHBcJ7RFrZ58+w4fqAI2xa9+Sie6ZK7Cqv6s2PZSZNLxTlBAD1tIVYko2W7i+lyDj779VfpSUSIODZnx7JkvQAvP1tbIqzuiJDzA0bSLgK0x0JlQvLvrt/GJWs7+MGBc3zq0R9xajRNIuwwlMqRjGqTzVjG461zEwCs7ojykas38vOX1CYAYWfS/NOqsf8LiRECg+E8Yve+fgYmcni+whJw/YATw2k64yH+/S9eUvU1hVV9xvNxLKnaUN624NxEjqG0q+v/pCbbMlarn7R+jxYXvdrWu5MDZ8ZAdNvKUkKW0JWIcHwoxZrOGDdtX89QyuXWLz7H4YEJHFvoTYQ5NjiBF5Q3s7EtYU1HlL++dXtNtfzjYYeO2PI0/8zE8t0LGQznIfftOUh7LMS6rhgh2yJQ2iwznHK5b89Bdu/rn/KaXdv6uPOGt5MIa5t8NbxA2/ltEXJ+wFjWr3qtAndctwXXV6Ryus5PKufhBgErk5Gy82Ihm4mcz5c/dhVf+/h7uOtXLyPrBdzz9AGO5/0Hga84NZLFLREBxxJWJiNs7onh+sGMIiCizT/ruuKs6ogaEaiC2REYDOcRBXt/wdxxcjiDZesJdKZcgPv2HCTsWKisPlYwlpS1khT9WBC6E6EpzufKENQbr1jL3oODRZNRyBLcito/qZzHqvYox4dSxbpAjz5/DCfftlLXICr/jJboUhUiumfBqvZY1XvRyrV/FhuzIzAYziPWd8XzBdUo2vwFIWxbxMNOMUGrQCF0tH8sw6r2aHFCUAACYXtyArVEcCxhTWeUnrwpp9p1CiGoj710gp1bulnXFedY/tzRtFvcJYxnXTJuwK9esa6sONzx4RQjaRdflYuAoCcsJ1/LIu36eIHipivXl92DSMimrz3K+u4YnfGwEYEaMDsCg+E8orRyaNbzi3X942GHg2fHyeX7ARdCQO/bc5CcN9llTKFX3GHbws53CSsQcSYrg6ZyXpnzuVrI6rnxDPfufot1XTE6YyHSro9Cm3UGJnKsTEa56V9MtonsH83wN88dZWA8N2UnYgGWLSTCDl2xEGMZj1XtMW66cvL1iYi2/7dK6eelhBECg+E8ohDFc/eT+/AD8FHYAoMpF0d0JzGBoolo/5lRRjM689cWQQl4gSLrBWzojnFiOFPM5J3I+UwMpOiOh2iLhrjjui1Fc9BzhweJOjpCqT0fujqScvEDVRSHWMjGCxSxkMNffuTdxTGfHcvyleeO8sRPThV9FJagY/pDwsCEixcoNnTEuP26C8paSloixc5fyzn8c74YITAYFpF6Sz/MhR8fHy7W5fEVePlynpLPBSg0fdE9h/MTb958ErIFpXwQ4fRoFqtQ0AeVTwqDobTLbdds4sfHh7l391t4QUAQQMbzOTmSBnQ4aNYPiDo2Sin8QOErRdgWTo/qc86NZ/nKD4/yzRIB6E6EuXnHelYlo/yfl05wejTNJas7ylb+epzLL/u3kRghMBgWiXpLP9Rz3YK4JCMOB/rHEBFCjoUVqOIk6wWKqKMnzUKCWdixSOd8nQGczyGwROiIaxPL4HgWX4ElerWtlMILFE/85BSHBlIESlcc9dDHsRTnxrM4tuBYFomIjeurYoewjBvQk4jw50+/yTd+fLI4tq54iJt2bOCGy1YXG9u8Z2vvlM8aC+vSz6Xd0wzzx9xNg2GRqLf0Qy1Uisub/eN4ga7zI1jFAm8FFDqSqKctxKaeNoZTOUbTru4/jPYD9CbDbOppA+DUSLrM5KKUPufQQKqYASwIIVtXm/MCxUTO59hgip62MBk3IJXziIYsJrI+I2mXUyMZXjs1CkBnLMTNO9bzy+9cM61tX0Roizi0x5yWqf3fajRUCETk/cA96A52Dyil7qpyzr8GPo/+HX1FKfXhRo7JYGgW9ZZ+mInCLuClo0MIsKojiojg52v7eAE4NvgVQpDztEP41EiWkbRHzvNRivxErncNXklbypeODuEH2s+gM44VyWiIoZRLxLHwfL2TgLwFCYg4woZuXcF0LOMyknIh//rCaDpjIT505XpueNeasv7BpYRsi/Z8+Qdj/mksDRMCEbGBe4GfA44Dz4vI40qp10vO2Qr8f8B7lFJDIrKwxlKDYQlRa1P42SjdBRSKup0czrCmU0f7BIGvQy+rNJUvfZjK+dgWxRLSgVI4ttCTCBd3KB/fdQFfePpAsVR0yBa8QLGlN8FQKsfARC5fj0IVM5J7EhFSOZ+hCR0CqkreOBG2+fWrNvCBy9dOKwDG/LP4NPJO7wDeVEodBBCRR4EPAK+XnPPbwL1KqSEApdT0qYoGQ4tTa1P4apT6AUbTLvGwTUcsStjWq3JER9+sSEY4PpQmbOl4+5xfPvlXohSELAvHErasSKKUYiTtlp0jIii04BQawlx/6Soee+kEPYkwo2mXrFew9TtEQzZHByfwSt7YEl1NdENXgpt2bJgyjoL5pyN2/tf+X4o0UgjWAsdKHh8Hrqo45yIAEfkB2nz0eaXUk5UXEpHbgdsBNmyY+ktkMMzGYkTrzEatTeErqfQDnBpJk3Z9Io5Nb1uEkyNpREHWC7AtoSseoicRZiLns77L5qdnxqcVg0J56FQuYN/pUWzRVUQL73vv7rcAiDpW0TRkW7oN5Wd/4WL+ao9+flV7jKFUjsFUjkMDE2WuCccSNnbHsSw4O54pe3/bEtqjIdpjJvu3mTR77+UAW4FdwDpgj4i8Qyk1XHqSUup+4H7QzevrfZOlMAkYmkejonXmQrUCbbNR6WSOOjY5P+DceJYtK7RT98xYBlFCXzLKv//FS8reY/NnvjnttQWKZR9cX+GiOD6UKv7N5LwAEd01rMCZUV1N9MKVbfy3X3sno2mXr754nK++eLzodAa9CxCgLxkpJqcVykEUwj/bo6b651KgkUJwAijN/V6XP1bKceCHSikXOCQi+9HC8PxCDWIpTQKG5tCIaJ3FpNLJvCIZ4cRQmqwXoPJ2/b5klDtveHvVz2Pl6/ZUoxAyCpMZvGk34K5vvcHZcV14qJqfYTzrsXvfWQ4OjPN/XzpBKqczkKMhi85YmLTrkcr5dEQdEhG7WA7iN67ewMr26Hnb+7dVaeS38TywVUQ2owXgJqAyIujvgZuB/y0ivWhT0UEWkFafBAzzZyGjdRaKyl3qzi3d7D04WHXXWulkTkZD9Cb9YjhmpYmp8tohC/zKBgN5FGALhEvCMv0g4NBAiohjYU3zWscS/viJ14sikQjb/Oq713HjFetoi+pxPndwkEefP8bp0TRrO/UYf/7SVQtzAw0LSsOEQCnlicjvAk+h7f9fVEq9JiJ3Ai8opR7PP/fzIvI64AOfVkoNLOQ4luIkYFhcFipaZ6Go3KUeOjfOc4cH6UuG6UlEpuxaqzmZQ7bNF266bMpiptoOWCEkwkLaDQiUXvl3xBy2rergpaNDxWSvSsKORSpb/TOkXa0Olui/p829CS5Z1V4UAYCdF/bwLy9dRTLq1NQrwNA8GvrtKKWeUEpdpJS6QCn1n/PHPpcXAZTm3yilLlFKvUMp9ehCj6G0GmOBZk4ChsWnam38GqN1GkHpLlVEd9qyBEbTHiIypUpooV9AXzLKSNqd0QxUee142KE7ESLn625ljkX+X4s7rtvClt5E0WHsBwFp1yfn61pD41lvdhNO/rWnR9Lc8/QBnjs4SDRf/XNDd5yuRNiIQAtw3hvq5hOyZzg/2LWtjxuPD/PA9w8xkfNJhG0+du3mppkGK3epOT/AEv1vgcpd62xO5pmKv4VtSyeFOaIdszIZUnr9pat485/fJOuV238sdDmIjBvosNGS58K27mJWKAnt+YrhtEtXXPjaj05w01Umsq/VOO+FYK4he4bzh937+nnspROsSEbYkF8MPPbSCS5b19mU34NKU1XYtsj5AeGSlXM9u9ZSc1DE1h3ESou/nRnNEnasshaRqZzHXd96g5Qb0NsW5tTIpA1IALGk6CUuiEDIEuJhCy8A1/dxHF1ewhLd0ziV8zk5nJ7HnTE0i/NeCGBuIXuG84elFjBQuUtNRh3OjudojzkoperetZZ+vr72KCeHMygmi7+5QcC6zvIuXrGQzYH+cdZ2RkmEw5wdy1LYFCgoizJKhG2+9NEddMbDANz8v57FD/L+hnzsv2XpPAZjcm1NjPHOcN5zbCg1pZxBMwMGKm3+m3vb+OR7L2RTT9usPoBqlH6+ZDTEms4oIUvIeAF9yShbV7RNsdMX/CW2ZeH6AZPNKSexBEKWbjbfGQ/rqqSxEJt6EnQlwoAuS6HQJaZtS4zJtUUxQmA471lqAQOl4Z0FU+Vl6zqBmctBTEfl50tGQ6zujLFjUzeP3H41n7n+YkbSLgf6x9h3epT9Z0YZSrms6YzRP5bh0LmULiFdgpNPBmuLhvj1qzbQm4ywoTtOT1uE3/mZCwjZNj1tIWwB1w+wRPj4rgvMzrtFkelCx5Yq27dvVy+88EKzh2FoIUpt6KUBA/Wsuuf7/qX9As6OZ3EsHS2Udn0CXSqIiGOxsj2CY1uMpl16EmHGc35ZXsHuff3c/eQ+XW5a6aqgq5IRsr5OLBtJuWTzE/PKZARESEYcTgylyHq6TLQlFHcIhTBQQReUKxSOA1jTEeU//PIl/MtLV0+b9zCd381k8y89RORFpdT2qs/NJAQiskopdbphI5sDRggMc6EwMTUiYGCmSa9ShN7sH9dlGyzJl4ue/PsTdO2dnkSYgYmcNs3YVrGG0BXrO3jh6HCxmUspIQsQQSl9DdcLsCxhbWeU0yMZvEDRl4zgBYrBfAtJ0IlhnfEQrheQcn06YiF6E2FyfoAXwJ03vB2gLiFttvAaqjMfITgNvAo8AvyfyhpAzcAIgWEpUJj8D/SPMZbxiIUs3Hz8vW1pMwnAPU+/WZx0Q7aUTeKVYZkFCjV68kVF6zIXJcK6DpHn6z7DBcu/yNRSERFbePijV7EiGeEP/u4VhlI52iKTtX9SOY++ZBRgSkJe4blHbr96yhhuvv/Zus43LA4zCcFsUUNrgfehy0P8FxF5Fi0KX1dKmTgxw7KkdMWbynp4fsCwF+DkTS6+UtzznQMEqnwSr1zJz1QRdLZzpiPr+mXlnws/lq73CkITACeH0rxjXQdnx7N0xkJlBeAKDnUFdWXnm2z+1mNGIVBK+egyEE+JSBi4Hi0KfyYi31FK/foijNFgWBJU6wrmBqo42Qbouvragdoc31stbxsoXV+oLWzzH77xGn/67f2Mpl08P2BFfgcA5Q716Up0VDOLLbWSHobZqTmPQCmVy9cEegN4N3Bxw0ZlMMzCYjsjp+sKZosUSzQXVt3NjL+o9a0VMJrRJS02dMeZyLqcHs3SP5Yt5jaEHbsYDlotO3/nlu7iPbEFXj46xEcffp7Veec1YLL5W4RZw0dFZL2IfFpEXgL+If+aG5RSVzR8dAZDFQqTcv9Ypqy0+O59jWtwV0ja8nyFH+i2jG4QFHsEF8i4/pRyDfXS6Or8YdvSSWP5RvRjGY/xbL5tpYKM5zOUcrnxirXFZMxqtY72HhwkZOsS16dGsvlKpsK5lIvKv89c8iIMi8+MOwIReQbtJ/gq8NtKqRcXZVRLGBMW13zqyRSe7vuq93s8NpTCFjg5ksFCm4GUKjcBLdRGoNEbitKaRr1tEc6NZ7EQbFvwRbFtVTupnMfeg4N8In9etez8z3791Xz11AlEwBIpZiV3xEJ0xsN861PXNfjTGBaC2UxDnwG+p1ot2aBBmCY3S4NanZFf+PZ+7t39Fn6giDgWnh/wucdf48bjwzz20om6vsdkxOGnZ8aKZZwLYgD6MWrycatgi44mSuV8HWHk6x0C1ObcLfgCcn5QbDOplN4JGOdwazGjaUgptQe4RUReFJGJ/H8viMgtizS+JUW1Er+l5YINi0MtmcKFfruBUjiWNukMTOTIeT4PfP/QjN/j7n393Hz/s7z7P/0jl33+KS77/FPsz4sA5Pv85t/HFh23b9vzN+gsdsfGQMGxwcnJWqHzGsYybk3O3UJ5b1uEIFD6PxS9bRHjHG4xZhQCEbkV+BTwh8AatJno3wKfFJHfaPjolhhLrWbNcqWW/gL37TmIF+iVqohgWYKFzuadyPnTfo+FXd+hc+OM5jN/RzPetNE4vtImkXCL1dyPOro3gUILkKCTyyyB0yOZmpy7Bd/B5t6EznmwdDayk8+XMM7h1mE209DvAL+ilDpccuxpEflV4FHgS40a2FLEhMVNz2L6TmopLX5sKEXEtvQElV9pi+gKmYmwjmSp9j0Wdn0D4x4WWkBc32dGlKK3LcrE4DwXBHNIIKtG6TXsfCKZqnh+VUeU40MpbEvYtqqd0bTLufFsPqNY36PPfv1V1u+Z3a9S+tzxoRR9yajxnbUYs2UWv66UuqTe5xpJMzOLTep8dZbifbn5/mc5PDDOwLirV7yinZiF4mgFH4HnB5wZzeIGAVtXtHF8OIVSwkTOxxIdBeNO0/h9oVkIEajEFvJiNnllS+Dtazo4cGYMhLI+BWfHMgylXNZ1xcq+yxuvWFu8Z0vlOzbUx0yZxbPtZ2fKHl52mcX1tAxcTixF38kd122ZtkLmJ953EXfe8HbCtsXx4QwIrOuMMZJxGc/qdo2CXkkvlgjAwomAbWmnb8TWOxrHsgjbVllYairnkYw6tEWcMhPbUMqlOxGa8l3O5lcxtDazmYYuFpEfVzkuwLI0AJomN1NZiiUFysxH1lTz0a5tfdy35yCbeuJFE9HBs+PYlu62ZVs6Pr4Vw+V2bOrhjuu28Adf/RGjGY8gX3G0+JkU9CWj/Ptf1Bv6UhPbSNqlJxEpu14sZDOR89lg/GPnLbMKwaKMwtDSLJbvpF4/xHSiXdrfN2ILfe1RktEQOT/QEUboFXXWC4oF41pBEBxL2NrXVizsdtHKdg6dG2cs45HzAyKORTLq0JXvNPbZr7865T5WKxiXdv0Z/SqG1mc201BMKXVEKXUEOF34Of949SKMz9AC1BLFM18WKpu49DpRx8INFCeHM4xlXMK2RaB0RM2WFW1cvLqdVR0RkMZn+84XAbriIf7o/duKx+64bgthx2ZVR5S3rUyyqiOKHyjOjmenvY/TfZcfu3Zzw79jQ/OYTQi+UvLz3orn/mKBx2JoURbDd7JQfojS6/S2aROIQtE/mqE95hAoSEYdRtM5DpwZ06UT1NLYEbRHHTpiUzfxAmxbleRPbnxn2T2v9r30JMJ0xKb6AAr3cbrvsuBXMf6x85PZTEMyzc/VHhuWMY30neze189LR4fwg4CIY7MiGSEZDZXF/tdqMir1Z7Tn/z09miHlBvgTLquTYTK+4vTg0omFEHRzeIB7PnQ5dz+5j4PnJgDY3BPnM9dfXAzhvPn+Z6uGdxa49u6nZ/XnTPddGv/Y+ctsQqCm+bnaY4NhwSmYcgq1bLKez5GBFJYlhGyhNxGuq+xHNX+GHyiijsWFfW2cHE4zmHIX8RPOjiWFpDWpOhnv3tfP+//HdzlwdpyQpdtdTncfTC6MoRqzmYbWicgXROR/lvxceLx2EcZnWOYUTDkrk1G8QOEFFAubZdyA48MZRtO5mk1GlTbwM2MZQCdYjWd1+ORSw88Xt4tWRO3ApFAeHkxh54u+nRrJ4geq6n1YDH+OofWYbUfw6ZKfK7O4TL9IQ8MpmHIkLIRGraolnofTHh1pt2jqqRbWWGo+agvbiAinRzNk3QAFHBlILfkt7smRDF/49n4+8b6Lisfu+tYb9I9lyLhBsQG9CJwdy7K5NzHlPtSSlW1YfszWoeyh+VxcRN4P3APYwANKqbumOe9XgceAK5VSRmAMRUpNGUFFFnxpJu7pkXRRCKoVoCs1H6Vdn9F0jmxJ4bqlLgK6h4Dige8fKgrB7n39HDg7ji26RlCgdOJcyBZyvprW5GNs/YZKZutH8PhMzyulbpjhtTZwL/BzwHHgeRF5XCn1esV5SeCTwA9rHbRh+XDHdVuK3bHCtlWs+yOF/8vP4DlfoZSq2g2rWv+CE0NpXT5ZKOvxuxQJ2xa2pTujTeQmxeu+PQcJWVa+IQwESifA5Xzt8zAmH0OtzGYa2gkcQzes/yH1RQrtAN5USh0EEJFHgQ8Ar1ec95+Auyk3QxkMQLkpYySVK06Eqvh/2tGlgJG0O20BOlt05nDODwjbFjkvIECXY1jK2wHHkmKt/yDfUawQGXR2LEtH1GE47U35y1yRjJjwTkPNzCYEq9Ar+puBDwPfBB5RSr1Ww7XXokWkwHHgqtITROQKYL1S6psiMq0QiMjtwO0AGzZsqOGtDecTpaaMa+/6DieGMygK0TQAOqN2um5YbWGbN89OYItgi+5NUPA0uEu8m0ygFBnXL6kmGhSTwc6NZxlMubRFHEbS2sk96Scw0d2G2pnNR+ADTwJPikgELQi7ReQ/KqX+fD5vLCIW8N+B22Y7Vyl1P3A/6Oqj83nf5c5Sa7VZ73j++IPv4NOPvcJYxsMLAhxLl00ozaitpDgpiv7PD5bG7C+iO5+NZrwpz0UdC0sglVcqK5/d7AXg+Yox30MFCi9QDKddbAHHsghQrO6I4eQjhs6XHcFS+70936ileX1ERP4V8GXg48AXgK/VcO0TwPqSx+vyxwokgUvRwnIYuBp4XESqlkk1zJ9mNH1f6PHs2tbHn9z4Ti7f0MXqjhiXb+iaklFbydnxLLZAzgvIugGBgnxHxppsnY1aW8dDFqMZL7+rmcQW8JVCRLAteMfaDtZ3xQmU7iB2dCjFsaEUIMXP4Ss90DUdMdpjofOqINxS+709H5nNWfwwerJ+AviPSqlX67j288BWEdmMFoCb0OYlAJRSI0BvyXvtBv7QRA01jnqavi/l8dQT9fKFb+9nYDxXnhmpJs1KIbt6SGopc92C9iZCZDyfrKf06r3kQnr+Fu3vzo+nEBQVKIjmxxWxLUbTLidHJjOdCyUvAqWwLYtA6U5sjiXTRk61Mkvt9/Z8ZDYfwUeACXRUzydK7I7691ep9uleqJTyROR3gafQ4aNfVEq9JiJ3Ai8opWaMSDIsPEutXHSjx7N7Xz//85/frDqRewF0RB3Gs1PNMvPFzmcChxyLsayu3On6Cq8k4geBjBdgW3ostgheSXhsMurgpVw64toXYCH5sNBJ0XIDBfkS056v8H1/2sipAq1oYllqv7fnI7P5CObViFUp9QR6N1F67HPTnLtrPu9lmJ2lVl6g0eO5b89BXF9N2/lrLOMx016gsOypdUdQeB+FnqTPjGbpiDmkcgFd8RCp3KTT1xLJd0yDiC04toXyApTSxzb3tvHhLd089tIJsl6u2CehGrZAIPr50yNptq5srzrBV+ZTzFaOY6mw1H5vz0daq+O2YV4stfICjR7PsVlWjIV5tdJGL+jJ2ZbaRaD03KDExDOc0j6AVL71ZSGqx8lnAPsKutvCbO5NsKknzrquOA/cciWP3H51seJnPGzjBYpA6bIRTsWA3UCLgCU6h2Dnlm7u23OQa+9+mpvvf7ZoS1+KneRqYan93p6PGCFYRiylVpsFE0Uq53F2LMvpkfSCj2d9V5ywLbNO5oWJW4DuuINtQUc8hIgQyUfvlFLNeezY1f+UFNoEFHYsLl7dzsaeOGHbKha6S0ZsNvW0Tft97NrWxxduupy1nXEk71DwqrTPtAQcW5jIetzz9JscOjc+xbF6bChFrKRe0Wja5fRIhucOD5YJxlJjKf3enq/M2Lx+KdLM5vWG6anH9rxYze6ffuMMn37sFYYm3BlNQKVY6Ek17Fj4atKEU2qbLzU1VWsOP+WaAm0RhxXJSJl5I5Xz6EtGix3FClS7lwCfePRlRjMeUuJYLh1T2LFwfe04Dtu6uU7p+wBFE0upAzpkCas7Y6YZ/XnOfJrXGwyzUm94X6NNFDkv4Nx4li0r2vj0z29jU2+i5hDQAAg7Nlk/oBAbUYjIKfUZRGyhOx7CsnSCWtiWKe9hic4MjoVswrbUZN6Y7l4CfOGmy7EtLT6VFCqyWvm6Q6XCVXCslppYzo1nAR231NcebRkzkaExGCEwzJt6J/ZKEwXMLwqk0JDlmru+w7/6ix/wf188zmjaJVCKFe2RmpvQF+bXtOsTBODmnbdBoBCBle0Rtq1sY+eWHpKxEF6g6xtFHIs1nTE2dMeL1xAgZFk6aSzqsHVlO3fe8HZClnCgf5zjQ2kS4allpWe6l7u29bF1RVuZKasUP+9H0MI0+addcKyWmlgyXkDIEtZ06n7NYCJxljNGCAzzpnJiH8u4nBpOT2t7Xt8VJ11S+RPmHgVyzz/9lNu/9ALPHRpgaCLH0YEJ7nn6AN/40Un+4zde56MPvcCRwZknt7Bt5at7auJhm864g6/0ytqxhZ5EmLBj85nrL+aO67YQD2tTj4guBndyOIMIrGgLF01Hpa8rrPwHJnIIOrv50LkJPv3YK2X3ZzaR/Mz1FyP5JjWRkFX2B6yg6IAO2TLjzsOxhEpXg4nEWb7MlkdgMMxKaXjfWMbl5HAGhXaGVgtRLK0oWuojqCcKxA8U3/rJKe7d/VY+sUqbaIZSLrbl8T++c6COawWUmvi1SNl0xR38ADpiobJidjff/2xx1R62LTxfQb4HwJYVbdi2MJH1p7zu+j/bkx9fPlxUwVDK5e4n9xXvTeFeer7i3HiWTH5XAnDZ558iZAsR28ILAvxAv6+lQOXVJ+rYhB3BD6YW4Sv1zaxqj3BiOMPxoTTdcY/RjIcb6F3C7n39LVV2xDB/jBAY5kzhD3b/mVHGsz7diRAjKZeCIaa3LVI1C3Q+zVFcP2Ak7TKe8Xjge4fwAoVjC0rpsgyBAj8/q6/rijGaccnmPLL+1GtF8nb7Sj9voCDrBeR8nXT2vT96b9nzpQlOvW0RTo6kkfxrUjmPkG3zsWs3sPfgIMeGUkUT2cFzE8W2k54fFMe778xYcfK947ot/OFjrzCccgFFiamf8ayHbQmJsE02C33JMOfGc3kT0aSZRynFSNqdMu7KDF0QTo2k6R/PEQ1ZrOuM4QaqqbkFrZrr0OoYITDMidI/2NUdMV0Jc8LNN0axQClOjqQ5N56lty1ctVNWPX/YGddnNO0ynvV47uAgjz5/jB+fGNbNWLypPoANXXH++rbtfPqrP+bEcIqz4zmAYsSNoOP3J7I+K5IRDp2bIFDkQzR1+QYBhtIe1979dNnKtHQHVCjpcGYsgyihLxllZz4RLOf5jGU8To9keOnoEF4+osfzA50VnEcpyia7FW0RxjMeWU8Vx6NLSoDyFWk3YEV+7JZoJ/Wqjklb/3QmnsoM3fZ8BVNf+WztSxaPt2LZEcP8MD4Cw5yodGquSEZZ1xUjGrLzJgtdR98LFCeGM7RF5rbmGM96nBxOc3I4XRSBe54+wOnRdNXMXx2pA//vrgtwbIvfes8moiGb7rieAEvDLgcndOnmWMierFBakgzmB6CUmhIJVZng5NhaAD6+6wIA7t39FqdH0vSPZfF8lbfH652HH0zNA7AEXN8v7hzGsh4X9rXh2ELIKs+DUOidhx8EZL2AtoidP6bLS5wd0+aeA/1jU/wz1XwzhXpGpRR8EgUnfGViWiNZ6EACQ22YHUGTaHU76P4zo2TcoNjopbctQjLqkPN8oCTNNv9vPfkqQaAYy3iMZvQOo5SH9h5mJO2Syk219Qg61PMjOzawa1sf3QmdsdseC3H3k/sYSbtFM1DYseiKhxhKuQxMZIk6ushbkDfXgA7TjOZFonRl+sjtV08xbRV2ASFbtM8hmCwM51iW7oRWxQwFWnROjWSZyNuvCjuOsG1N+znPjrtEHatsNzae8cj5iu5EiJ5EZIpZ5Y7rtvDpx17hxFC6WMJbRCfPlZJ2fdoiTlNMNKacRHMwO4Im0OpldXfv62c86+dbPWonbcEMZFsWazujOLbg51fLazujZS0Wp8P1AwbGsxwdTDEwkS0TgZPDaf7rkz/ljdNjZZNjaQilYwu3Xr2Rf3v9xazqiBJ2LHbv6+eub73B/v4xHclj6S5fSulJviseYnDCJRl1ENFCErJ0LL5lCb1tkeL1S1emhYl1XVecY0Mp/vK7ehdwdDClTTj51/hK4fkBGTeYNYR1LOOV7TjaY07V1xSOreqIlu3GRIR1XTF626LThvGq/E0T0fUuoo52dlfmN6h8OYvFLkdhykk0B7MjaAKtbge9b89BuhMhBsZdHbJoAYGOgNncE8cNVDGrFcozW6tRav+v5NRImr959ihPvX5Gm5yg2Ki9gJ3PBF7VHuW1U2PE8vH5pYJri5AL9GrfEu0LODuWZXNvAs8P2NzbhuePkvMVYUe3soyH7aIPYDTtcmYsg1Jw8/3Plu0AbIF0voGMFhFtCgI9zqDG3ZDK39vSHce5sRy+UtqvkVe9QGlHd8EnAFqkJnI+G6Yxq+ze188nHn2ZVM4n4lis7IjSHgtp57alO5od6B8HdAjsm2e14EUcmxXJCMno4vQ4mE8ggWHuGCFoAq1eVvfYUIqeRISIY3N2LEvO12GH8YjDZ66/uObQ0Imsx0jaJVNht37u4CAP7z3CoYHx4gQL0JeMcOGKBHsPDhaPKXTcfDLikIg4ZfewILh+oLBFsETlm7vo2j85P2BgIkvOVxwbSpVV7SyISCrn4fkBJ4YzAKztjNI/luHe3W/RnQhhic2xocleAV6giIZslPKrmoFmQqBsx1EZ8un5AWdGdUhpzle8fmpU+xCUypewFgYmsvS2TYpuqZlnIufhWJM7ONDJbqdH0sQjIdZ1xYqfVZfG1p/n5HCGNZ16t7QYJpp6AwkM88eYhprAQiZUNYPC+JPREFtWtLFtVTurO2Ns7UvOWiAsCHRo47HBFGdGM1NE4B9fPc2d33yd10+PFkXAEvjgO9fw8G/tIJUL6G0LlWfw2sJEzp9yDwuOx3A+Zr/UEZx1A4JA0T+mSzwPjmd57vAAd3z5Rb7w7f1ln+P0aBbH0mYXEE6PZMh6AadHshwfSpftThQU6/0Uqo3WimMx5XegMI6wbXF8OIMfBLqJDdrxnPECsr7eMSQjNv1jOc6NZ6qaeaKODQiWJVgI58azpF2fnD9pBjo3niuW1AiK90xxeiRjTDTnMUYImkCr20FnG/+ubX08cvvVfO+P3ssjt1/Nrm195fb/8ewUJ3D/aIY/+/YB7n7qp0UfgG0JfW1h1nREOTyQIuxYnBpN0xWPsK4rRsjWxdUsgYw3dedREKzetghuEJQVniuEY0YdYSIb5LNxLQKluHf3W8W4/kduv5oVyQgX9rWhFJwcSeP5OqxTQdVKoIVjb1uZJOxYhPPjnE0UYiGb4VRuSpTOrm19dMbDbOqJY1lW1YJzIdtifXeiGFZaKsLjOZ9YSJt4VMFUJYqsF+DmTWGFSJ1cvsaSY2nBcPLVWxWYgnTnMcY01ARa3Q5az/hnsv+DttN/5bmjPPGTU8UKnralC7p1xEJYIigUp0fT2JawoTvO4ESOzngYS/SqNuspEmGnOFEVIrIO9I8xlvHoiofKWlJGHYtVHVGODaVIuzrvwcob4AvmkFJ/TSGSpdApzLIEW1HWerISEeGP3r+Nu5/cx4H+cWyLfIcx/SJBO7c9X+dARByLSMgm5wdVo3SODaWwheJnKK2AWghPBZ3gVplMtn6PHn8yGmJNp77nGS8o3rP79hwsRuqEbUsLmdL3acuKtqKPp1V+Pw31Y4SgybRWEfBJZrLjKqWYyGkBqDT9FDg3nuWR547xDz8+WRSA7kSYmGOD6Il9POsxlMqR8wJiYZu3+sf5+K4Li7b7ZNTByWcHl4rAZBmFKCF7MtEtHrLoa59MvIrYFil3ssqoHruelEt9DYWSGNl8a0ntcLYIWQElLoyyyTkIFJ/9+qskIw7xkEXOV/hKEbK0T2Nle4SeRKToQ4mHLNxATRtAsL4rzsvHhsq6oBXwAkU87yCvZmIsLenRFnGwrfJ7BhSf720LF/0hq9oiLbdbNcwNYxpqAq0ePgpUTTbyA8VwKsexwTT9Vez/AAPjWf78n9/kI3/9HF97+QSur+iKh/idXRfwNx/dwe+990ICBYMTWfrHMrh+gKCdmp//xusAM/ogSiOyxjIeo2kPP1CICB3xUFmkjW4+o23thSqjAYpk1CmbTAt2+kKnMCdftXNt12S10WjIIuzofAE7H4baGQuR8wMiIZtNPXFWd8TYvqmHT/3s1mIzmrBtEQ9ZHDg7wanhNGMZt/i+pQEEd1y3hew0IagKLQAH+scYSbtTJu3Z/DalzwcKtva1ceGKBIHCNIFZJpgdQRNo9fDRynowZ0bT/Lu/f5Xfe++F7NjcXfU1gxM5Hn3+KI+/copc3rzRFQ9x05Xr+eV3riGat1Hv2NLNJ9nKHz/xOoGiaNtORnWo491P7qMzHp42Ea8QkVVovGIh2BbkPO0YBoor8ZBt88F3ruaJV8/gBbqcdDIaKqsWWqDQKayyoU4yYpP2AlxfZ+iqvJO1EONf+I67EhGe/P3JBjSfqLiP0XwUUyFCJxkNla3ud23rIxa2qyaYQb7dppreOT1bJI6J1FneGCFoAq0ePloQsohTWCFbuOLz6HPHpgjBUCrH3z5/jK//6GTRvt0RC/GhK9fzgXetmVJOIGRb/NK71vDnu99kU09osvQD4PkBhwdSbOqJT5vtWrDnnxnVFTwLRpSIY9EZDzGR9XGs8qqcH8j7FGbzd1Tzjfz7X7ykeE+OD6XoH8uytiMyJca/2ndbuiAoFK9TKPpHM0XzTakgxcM2rhdgWbrfcdYLUEr7CC5e3Q40t06QoXUxQtAEWjmNPggURwYmaIs4eCWRP9GQxenRyXj64RIByOQFoD3q8KEr1/PBd60tJn0VsETozDuIRaTqPTozmp11J1Uoo5CpcKoWVvwh25pSlbOe1fB05xaO3Xz/s/SPZcqem+67fe3kCGMZD4Ve0ScjDl4+JLQvGZ0iSFv7khweGGc07ekOZGoyU7pALGQX6wy1avkSw+JjfARNoBXDR3NewNmxLEcGU/Qlo1PyIDJuwKr2GCMpl/v3HOTDD/yQv33hOBkvoD3q8LFrN/OV376Km3dsmCICyWiI9d1xOuPh4g6g6j0KAlYmI2WvrVxt79rWR08iXGw4L6Ibz9iWcGY0O2exrbUAW63f7Re+vZ/RvAjke9IzkvGIOBY7NnUXw24rrx2ybVZ1RHnbyiSRkAUIK0ruycBElrGM19L+J8PiY3YETWAphI/WWvQuldPZv+kS2/RNV67nnqcPkHZ9oiFLF5/zAjrjIT78wA+LIpGMOvzau9fxK5evJVGl+mgsbNOdCBNxprZs3LWtjxuPD/PA9w8xkfNJhG1WJyM4FZUyq622x3M+67tinBzJYKHNKEE++7Yesa3Wb6FaMbfKcdfy3T7w/UPFUhmlDuCh1FRn73TX3tQdZ2BCJ4AppUi7PoMTLl3x0KL5n1q9eKJBY4SgSTTTOTdb84+Zqn8WegFkXJ+cF+BYEA05DKdddu8/C0BbJC8AV6ytWn46ZFt0J8JVxaF0jI+9dIIVyQgb8o7Z0bRLJj1ZOnq68hUFs9KafGXOXD7Td0t3ouZ7XnqPxrM+WS/g1EiWoQmXVR3Rsj7CldTy3U7kfEK2EATabFVRrHVaKq+9u8K/MZzKlRXKg8b5n0wTmfMHIwTLkOmilv7yu29x6boOxjNe1UJphV4AjiV0xUOcG88xmvEYyegdQCJic+MV6/jVK9bRFp36q2WJ0BUP0x5zypzAtY4RtJmnMx6ecbVdiJsP2cLm3kRRMD5z/cV13yM/UGVJXNl8ZM/qjsi8JtdEWAuZ5DvPFHwZllDXZFopDAUfxWL4n1o9+s0wSUOFQETeD9wD2MADSqm7Kp7/N8DHAA84C/yWUupII8dkmBq1FORj448MTDCadqd93aPPH8NCF4sbTrvFGjuWwEeu2siN754UgMLO4dRomtXtMX7zPZv4pXeuwbZmFoDpxgh6ZTuSdvnWp66b8bULYXorvP+hcxPFrmaADtEU7bi+fENXzder5GPXbuaep9/ED8p3XD2J8LS7jWpmGPKfs3CsUBV1Pv2ga6XVo98MkzRMCETEBu4Ffg44DjwvIo8rpV4vOe1lYLtSKiUivwP8V+BDjRqTQbO+K86Z0TTRkFNMpkq7PqvaY9O+ZjzrceDsGOmcXyYAnbEQji3c9p5NxXNLdw6dsRCjmRx/+k/76YiFap6M5xtZNV/TW+H9c36AI+DmbfnasVu/v6GST7zvIgD+x3cOoPKlsXsSYVZ1xFBKTZlMq5lh/vCxVxB0y8nCscdeOsGNV6xl78HBhvufWjn6zVBOI6OGdgBvKqUOKqVywKPAB0pPUEr9s1Kq8Bv/LLCugeMxoCtj3rxjPWk3YCzjEqiAtOvjBYqbrlw/5fyJrMeXnj3Crz/wQyayfrGvb3c8xOaeBImIw5qO8j/8R58/RsgW2vPJWYlIqO6mJs2OrCq8vy26Vr+TryRq5YuxbV3RNu/J9RPvu4irN/dwwYoEb1/TwaoOLcTVJtPK1qDxfAmOsYw3pXnM3oODU4r+NYJmf0eGhaORpqG1wLGSx8eBq2Y4/6PAt6o9ISK3A7cDbNiwYaHG15LMNUojnfPzLR49Ll3bwSffu5VHnz/G6dE0q9pj3HTlenZsmUwGS+U8vvbyCb76wnFGM7pgXNi2CDlCRzREImKTcYMpAmJbQv9Yhu5EuMwPUK/JoNmRVYX3v/vJfezvHyfkWKzNRy2V+hvq/T4qz6/VlFPNDFPYzZWymKaZZn9HhoVjSTiLReQjwHbgZ6o9r5S6H7gfYPv27a1ap23e1BuloZRiLOsxkpoa/bNjS3fZxF8gnfP52ssn+LsXjhUFIOpYfPDytXxo+3p+enqsqoCICB15E8XGnsSCmAzmat6Zq1hWe923PnVdWWROaaLXF769n3t3v4WfT1bz/GDG76Pa91erKaeaGca2BFS5z2UhTTO13EdTmuL8oJFCcAIotTWsyx8rQ0TeB/w74GeUUtkGjqflqTVKw/MDRvKlnwvtHWcj7fp8/eUT/O0LxxnJO4wjjsUH3rWGD125nq54GKguIG0Rh65EmFA+xr+02uVCOSxrndznGtI42+sKry2M4w+++iMGJ1ws0eGwnq8YmMjRkwhPGzUz3fdXMOXMRLV72hZxEGiIY9iEhi4vGikEzwNbRWQzWgBuAj5ceoKIXA7cB7xfKWVSH2dhtiiNjKvNPxPT1P6vRsb1+fqPTvK3zx9juEQAbninFoDuRHja10ZCNj2JcLFgXIFqJoOdW7q5b89BPvv1V+tOPKpnUpprSGMtrysdR6EZve5JnF+dB7oB/XSmmflE2dRS52ghTTMmNHR50TAhUEp5IvK7wFPo8NEvKqVeE5E7gReUUo8DfwK0AV/N25OPKqVuaNSYWp1q5oFUzmNVR5TjQ6liVc9ayLg+33jlJI8+f4yhlBaAsGPxy5et5uYdG2YUgJBt0ZUIV00WK1C5ip7P6rKeSWmuk20trysdRy5fHht0D2TbsouF4KYzzTQqEqoRE7MJDV1eNNRHoJR6Anii4tjnSn5+XyPf/3yj1DwQdSwmcjrj9VcvX1ezCGRdn2/8+BSPPHe0KAAhW/jly9Zw84719FRkpZZSWRiuGtVMOPNdXdYzKc11sq3ldaXj0H2QA/xA5ctE6MYztiXTmmYaYTJbaArf39mxLOfGsqzqmGzkY0JDz1+WhLPYUBu7tvXxWS/gvj0HOTGcqhrtMx1Z1+cffnKKR547xuCErssfsoVffIfeAaxITi8AoAvDdSfCMyaETbfyT+U8VrVHy86daXVZKSbJiEPa9Wua3Oc62dbyulKxKJSNtiwIAh2W61gWH991wbTiNpcom+l8I42o8VPe3S3CieEMx4fSrO1UxWippSRahoVDKsPPljrbt29XL7zwQrOHsagopRjPeoxmPLLTtH6spJDZe3IkRci2Gc24jOWjgEK28AuXrubDV80uAPGwQ1ciVLUwXCXVyhukch5nx7KsSEamHO9LRqc4SUsno8KEPJJ2i4lTpcdWtEUYy3pTJsLK+jv1Rg1N97rKsZ0bzzKUcklGHbb2JRc8dLLavXB9xY1XrOWxl05MOT7fTmKV399o2uXMWAal4IoNXSY0tMURkReVUturPWd2BEsYzw8YzXiMZdyao39Ai8CffWc/GTdgPOvhBXoHYIvwC5et4td3bKCvYoVeSci26GkLl03eszGdCSec7ytcyyp9uhpDIUvoSuj6PomwjcC0jd7nGtJYSxev0hX95t427mrg5DidSe2B7x8qE9aFcuRWfn/tsRDJqMNI2p01qsnQ2hghWIJk8pU2J3L+lISh2XD9gD//5zc5O5bDL3ltImyzsTvB7+dLG0yHbQldiTDt0dCM51VjOjv71pXtRV/BbKv0mWoMFVo93nz/szM2el9omlVqebp7MZHz2VARqbUQjlxTMmL5YoRgiVBI/hpNu3VF/xRw/YCnXjvNl589Sv/YZDpGR9ShOxHGsYXB1PRpGiJCe9ShKx7GqrEwXCUz2dlrXaXX67QtcD6WWp7uXhQqly70hN0KzmxDYzAdypqM5wcMTuQ4Opji3Fi2bhHw/IBv/vgUt3zxOf77Px0oikAibLO5J87K9igh2yp2EKtGPOywtjNGT1tkziIAedPJDW+nLxllJO3Sl4zWbbeupX7N+q74lA5pi1FqubSeTz11k+bKdPfiY9dubkiNn4X4/gytidkRNIn5mH9AC8A/vX6GL//wKKdGdI9cS+DnL1nFO9Z28OUfHtGN5W1VtSYQzM0PMBvzLTlQS2TNYq5ca919NMJ8NNO9uGxdZ0MSyUzJiOWJiRpaRJRSTOSLv9Ua/VOJHyj+6fUzfOnZI2UC8HOXrOQjV21kbZde9ReihqoVlaunQcxSZa6RQfUyXSRUacTTdNE9ZjVtWErMFDVkhGAR8PyAsYwuGewF9dv/QQvAt9/QAnByeFIA3rutj1t2bqzZLNIWdehJRGpuELPUmc9KvJbX1jLJ1yIWBkOzMeGjTWK+5h/QAvD0vn6+9OwRjg+lAV0X/2cv7uMjV29kQ3dtAhAN6UbxlXWBWpn5OHJrfW0tpipTjsHQ6hghWGDmkvxVDT9Q7P5pPw/vPcKxEgHY9bYV3LJzIxt7EjVdp5ZG8a3KfEpX1PPa2ezmJuzS0Oqcf7NDk/ADxWjanZf5p3Cd3T89y5eePcLRQb2iLAjAb+zcyKYaBeB88APMxnxW4gu5ijdhl4ZWxwjBPMl6PqNpj/GsN2fzD0CgFN/96VkefvYIRwYmJ6OfuUjvADb31iYAUFtdoPOB+azEF3IVbzp1GVodIwRzZCLrMZpxSefmbv4BLQDfO3COh/ce4dC5ieLxf7G1l1t2buSCFW01Xyse1sljYWd5pIfMZyW+0Kt4E3ZpaGWMENRBECjGMloAKls/1n0tpfj+m1oADp6dFIBrL+zl1p0buaCvdgFoRD5AKzCflbhZxRsMk5jw0RrIeQGjGZfxjEcwz/ullOIHbw7w0N7DvFUiAO+5oIdbdm5k68pkzddaDn4Ag8GwMJjw0TmSynmMpj1SudpbP06HUopn3hrgob1HeLN/vHj86i3d3HbNJi6qQwBg+fgBDAZD4zFCUEEQTBZ/m6/5B7QAPHtwkIf2Hmb/mUkBuGpzN7des5Ftq9rrul40ZNPTFq6pP4DBYDDUghGCPK4fFMM/52v+AS0APzw0yEPPHOGnZ8aKx3ds7ubWnRu5eHV9AlBLn2CDwWCYC8t+Vknna/8shPkHtAA8d1gLwL7TkwKwfWMXt12ziUvW1CcAtfQJNhgMhvmwLIWgUPt/JLUw5p/CNV84MsRDzxzm9VOTAvDujV3cunMjl67tqPuaxg9gMBgWg2UlBAXzz3jWq6v140wopXjp6DAPPnOY106OFo9fsaGTW3du4h3r6heAWFjXBTJ+AIPBsBgsKyEYGM8tqAno5WPDPPTMEX5yYqR4/F3rO7n1mo28c11n3dc8n+sCGQyGpYuZcebAj47pHcCPj08KwGXrOvjNazbxzvWddV/PtoTOmMkHMBgMzcEIQR28cnyYh545zI+OTQrAO9a2c+s1m7h8fWfdk7iIkMz3CTZ+AIPB0CyMENTAT46P8ODew7x8dLh47NI17dx2zSYu31C/AMDyqwtkMBiWLkYIZuDVEyM89MxhXiwRgEtWt3PbNRt598auOQnAcq0LZDAYli4NnY1E5P3APYANPKCUuqvi+QjwMPBuYAD4kFLqcCPHVAuvnxzlwWcO88KRoeKxbauS3HbNJq7cNDcBMH4Ag8GwVGmYEIiIDdwL/BxwHHheRB5XSr1ectpHgSGl1IUichNwN/ChRo1pNt44NcpDzxzmucOTAvC2lUluvWYjV23unvME3h4LGT+AwWBYsjRyR7ADeFMpdRBARB4FPgCUCsEHgM/nf34M+HMREbXIJVH3nR7loWeO8MNDg8VjF61s49adm7h6y9wFwOQDGAyGVqCRQrAWOFby+Dhw1XTnKKU8ERkBeoBzpSeJyO3A7QAbNmxYsAHuPzPGg88c5tmDkwJwYV8bt+7cyDUX9MxZAEw+gMFgaCVaYqZSSt0P3A+6H8F8r3fgzBgPPnOEvQcHiscuWJHg1p2beM+FcxcAUxfIYDC0Io0UghPA+pLH6/LHqp1zXEQcoAPtNG4I+8+Mcd933+IHb02+xZYVCW7ZuZFrL+zFmsfk3RZ16I6HcWwTDmowGFqLRgrB88BWEdmMnvBvAj5ccc7jwK3AXuBG4OlG+AdGMy6f/uorPPXameKxzb0Jbt25kWu3zk8AIiGbnkSYaMj4AQwGQ2vSMCHI2/x/F3gKHT76RaXUayJyJ/CCUupx4K+BL4nIm8AgWiwWnGTE4dhgGoBNPXFu2bmJ6y6anwA4lkV3m+kPYDAYWp9l07P4B2+e4+DZCXZe0D0vAbBE6IiF6IwbP4DBYGgdTM9i4D0X9nLBirZ5VR81fgCDwXA+smyEYD5EQzofwPgBDAbD+YgRghkwfYINBsNywMxwVTD5AAaDYTlhhKAC4wcwGAzLDSMEeYwfwGAwLFeWvRCYfACDwbDcWbazn4jQafIBDAaDYXkKQVtEt4k0fgCDwWBYZkIQcSw64zHjBzAYDIYSlpUQdCXCzR6CwWAwLDmMbcRgMBiWOUYIDAaDYZljhMBgMBiWOUYIDAaDYZljhMBgMBiWOUYIDAaDYZljhMBgMBiWOUYIDAaDYZljhMBgMBiWOS3XvF5EzgJHmj2OGukFzjV7EE3CfPblifnsS5eNSqkV1Z5oOSFoJUTkBaXU9maPoxmYz24++3KjlT+7MQ0ZDAbDMscIgcFgMCxzjBA0lvubPYAmYj778sR89hbE+AgMBoNhmWN2BAaDwbDMMUJgMBgMyxwjBA1CRGwReVlE/qHZY1lMROSwiPxERH4kIi80ezyLiYh0ishjIrJPRN4QkZ3NHtNiICJvy3/fhf9GReRTzR7XYiEivy8ir4nIqyLyiIhEmz2mejE+ggYhIv8G2A60K6V+qdnjWSxE5DCwXSm1lBNrGoKIPAR8Tyn1gIiEgbhSarjJw1pURMQGTgBXKaVaJfFzzojIWuD7wCVKqbSI/B3whFLqweaOrD7MjqABiMg64BeBB5o9FsPiICIdwHXAXwMopXLLTQTy/Czw1nIQgRIcICYiDhAHTjZ5PHVjhKAx/Bnwb4GgyeNoBgr4RxF5UURub/ZgFpHNwFngf+dNgg+ISKLZg2oCNwGPNHsQi4VS6gTw34CjwClgRCn1j80dVf0YIVhgROSXgH6l1IvNHkuTuFYpdQVwPfBxEbmu2QNaJBzgCuAvlVKXAxPAZ5o7pMUlbw67Afhqs8eyWIhIF/AB9EJgDZAQkY80d1T1Y4Rg4XkPcEPeVv4o8F4R+XJzh7R45FdIKKX6ga8BO5o7okXjOHBcKfXD/OPH0MKwnLgeeEkpdabZA1lE3gccUkqdVUq5wP8FrmnymOrGCMECo5T6/5RS65RSm9Db5KeVUi23QpgLIpIQkWThZ+DngVebO6rFQSl1GjgmIm/LH/pZ4PUmDqkZ3MwyMgvlOQpcLSJxERH09/5Gk8dUN06zB2A4r1gJfE3/PeAAX1FKPdncIS0qvwf8Td5EchD4zSaPZ9HIC//PAXc0eyyLiVLqhyLyGPAS4AEv04KlJkz4qMFgMCxzjGnIYDAYljlGCAwGg2GZY4TAYDAYljlGCAwGg2GZY4TAYDAYljlGCAyGOhERP19l81UR+YaIdOaPbxIRJSJ/XHJur4i4IvLnTRuwwTALRggMhvpJK6XepZS6FBgEPl7y3CF0wcECvwa8tpiDMxjqxQiBwTA/9gJrSx6ngDdEZHv+8YeAv1v0URkMdWCEwGCYI/na+z8LPF7x1KPATSKyHvBpwbLEhuWFEQKDoX5iIvIj4DS6rMY/VTz/JLrcwk3A3y7u0AyG+jFCYDDUT1op9S5gIyCU+whQSuWAF4E/QFchNRiWNEYIDIY5opRKAZ8A/iDfnaqUPwX+SCk1uPgjMxjqwwiBwTAPlFIvAz9Gl2AuPf6aUuqh5ozKYKgPU33UYDAYljlmR2AwGAzLHCMEBoPBsMwxQmAwGAzLHCMEBoPBsMwxQmAwGAzLHCMEBoPBsMwxQmAwGAzLnP8f2AYBO6BquIgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "for var in features.columns:\n",
    "    sns.regplot(x=data[var],y=prices)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All three scatterplots above confirm our intuition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Developing a Model\n",
    "In this section of the project, we will develop the tools and techniques necessary for a model to make a prediction. Being able to make accurate evaluations of each model's performance through the use of these tools and techniques helps to greatly reinforce the confidence in your predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Define a Performance Metric\n",
    "For this project, we will calculate the [*coefficient of determination*](http://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination), R<sup>2</sup>, to quantify our model's performance. The coefficient of determination for a model is a useful statistic in regression analysis, as it often describes how \"good\" that model is at making predictions. \n",
    "\n",
    "The values for R<sup>2</sup> range from 0 to 1, which captures the percentage of squared correlation between the predicted and actual values of the **target variable**. A model with an R<sup>2</sup> of 0 is no better than a model that always predicts the *mean* of the target variable, whereas a model with an R<sup>2</sup> of 1 perfectly predicts the target variable. Any value between 0 and 1 indicates what percentage of the target variable, using this model, can be explained by the **features**.\n",
    "\n",
    "Let's define a function that returns the r2 score for given true and predicted data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def performance_metric(y_true, y_predict):\n",
    "    \"\"\" Calculates and returns the performance score between \n",
    "        true and predicted values based on the metric chosen. \"\"\"\n",
    "    \n",
    "    score = r2_score(y_true,y_predict)\n",
    "    \n",
    "    # Return the score\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goodness of Fit\n",
    "\n",
    "As an aside, assume that a dataset contains five data points and a model made the following predictions for the target variable:\n",
    "\n",
    "| True Value | Prediction |\n",
    "| :-------------: | :--------: |\n",
    "| 3.0 | 2.5 |\n",
    "| -0.5 | 0.0 |\n",
    "| 2.0 | 2.1 |\n",
    "| 7.0 | 7.8 |\n",
    "| 4.2 | 5.3 |\n",
    "\n",
    "Let's use the `performance_metric` function and calculate this model's coefficient of determination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model has a coefficient of determination, R^2, of 0.923.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the performance of this model\n",
    "score = performance_metric([3, -0.5, 2, 7, 4.2], [2.5, 0.0, 2.1, 7.8, 5.3])\n",
    "print(\"Model has a coefficient of determination, R^2, of {:.3f}.\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above model has a coefficient of determination, R<sup>2</sup>, of 0.923. Considering that the worse score for R<sup>2</sup> is 0, and the best score is 1, a score of 0.923. This means that the model successfully captures more than 90 percent of the variation in the target variable. Though it is important to note that how good the score is, depends on the domain of the problem. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle and Split Data\n",
    "\n",
    "Now we'll take the Boston housing dataset and split the data into training and testing subsets. Typically, the data is also shuffled into a random order when creating the training and testing subsets to remove any bias in the ordering of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training and testing split was successful.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Shuffle and split the data into training and testing subsets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features,prices,test_size=0.2,random_state=100)\n",
    "\n",
    "# Success\n",
    "print(\"Training and testing split was successful.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By spitting a dataset into training and testing subsets, we can train our model on the training subset, and then feed it with unseen data from the test subset to evaluate the performance of our model. \n",
    "\n",
    "Training and testing on the same data doesn't give us a genuine evaluation of the model, at it has already seen testing the data when training, and thus might not perform well in real-world scenarios where we often deal with unseen data. Related to this is the problem of \"overfitting\", i.e. the model can be really accurate on the training data, but perform poorly on the training data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Analyzing Model Performance\n",
    "In this third section of the project, we'll take a look at several models' learning and testing performances on various subsets of training data. Additionally, we'll investigate the Decision Tree algorithm with an increasing `'max_depth'` parameter on the full training set to observe how model complexity affects performance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Curves\n",
    "The following code cell produces four graphs for a decision tree model with different maximum depths. Each graph visualizes the learning curves of the model for both training and testing as the size of the training set is increased. The shaded region of a learning curve denotes the uncertainty of that curve (measured as the standard deviation). The model is scored on both the training and testing sets using R<sup>2</sup>, the coefficient of determination.  \n",
    "\n",
    "_Note: The section uses helper functions supplied for the purposes of this project and available in the 'visuals' module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce learning curves for varying training set sizes and maximum depths\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  # suppress warnings\n",
    "vs.ModelLearning(features, prices) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the learning curve for the model with max_depth = 3, \n",
    "\n",
    "- The score of the training curve decreases as more training points are added. This happens because with fewer training points, the model can modify its paramters to better approximate the targets; but as the number of training points increases, perfectly fitting them becomes more difficult, and the training score goes down. \n",
    "- The score of the training curve increases as more training points are added, but there's a slight dip after adding more than 350 training points. The score starts lower because the model has not yet learned enough to predict test points. As the model receives more training points, and hence, more information, it is better suited to predict unseen data.\n",
    "- The training and testing curve seem to be converging to a score of 0.8. This usually happens when the model has stretched its limits of extracting information from the training data even though more training points are being added. So the score stabilizes.\n",
    "\n",
    "Therefore, having more training points might not benefit the model (with max_depth of 3)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Complexity Curves\n",
    "The following code cell produces a graph for a decision tree model that has been trained and validated on the training data using different maximum depths. The graph produces two complexity curves — one for training and one for validation. Similar to the **learning curves**, the shaded regions of both the complexity curves denote the uncertainty in those curves, and the model is scored on both the training and validation sets using the `performance_metric` function.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.ModelComplexity(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bias-Variance Tradeoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- Model trained with max_depth of 1: The model suffers from high bias at this depth. Looking at the graph, we can see that both training and validation scores are low, and similar. We can say that the model is over-simplified and is not capturing the underlying relationships present in the data for both training and validation datasets. \n",
    "\n",
    "- Model trained with max_depth of 10: The model suffers from high variance at this depth, and is overfitting on the training data. In the graph, we can see that the training score at this depth is almost equal to 1.0, while the validation score is lower, at around 0.7. The curves also seem to be diverging away from each other at this point.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In my opinion, a maximum depth of 3 results in a model that best generalizes to unseen data. That depth is the sweet spot for model complexity, as our model performs similar on training and validation data, while the overall score for both is still relatively high at between 0.7 to 0.8. A depth lower than that gives us poor training and validation score, while a higher depth overfits on the testing data, leading to a lower validation score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "\n",
    "## Evaluating Model Performance\n",
    "In this final section of the project, we will construct a model and make a prediction on the client's feature set using an optimized model from `fit_model`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be using **Grid Search** and **Cross Validation** techniques in this section.\n",
    "\n",
    "### Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grid search technique is a systematic way of going through different combinations of parameter values while cross validating the results to determine the parameter combination which gives the best performance based on a scoring technique.\n",
    "\n",
    "In order to optimize a learning algorithm, we can apply grid search by specifying the parameters, and the possible values of those parameters. The grid search then returns the best parameter values for our model, after fitting the supplied data. This takes out the guess-work involved in seeking out the opitimal paramter values for a classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The k-fold crossvalidation training technique is a way of splitting the dataset into *k* partitions of equal size, and then running *k* separate learning experiments on the training data. In each of the experiments, we chose a training set of the size of k-1 partitions, train our model on that partition, and evaluate the results on the remaining test data. The results/scores for the k experiments are then averaged out. \n",
    "\n",
    "This technique is benefitial when using grid search to optimize a model because it allows us to look for parameter settings that perform well for different test sets. If we had a single testing set, it's easy to tune a model to perform well for that specific test set (and result in overfitting on the test set in this case), while cross validation allows us to generalize the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting a Model\n",
    "\n",
    "Now we'll bring everything together and train a model using the **decision tree algorithm**. To ensure that we are producing an optimized model, we will train the model using the grid search technique to optimize the `'max_depth'` parameter for the decision tree. The `'max_depth'` parameter can be thought of as how many questions the decision tree algorithm is allowed to ask about the data before making a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def fit_model(X, y):\n",
    "    \"\"\" Performs grid search over the 'max_depth' parameter for a \n",
    "        decision tree regressor trained on the input data [X, y]. \"\"\"\n",
    "    \n",
    "    # Create cross-validation sets from the training data\n",
    "    cv_sets = ShuffleSplit(X.shape[0], test_size = 0.20, random_state = 0)\n",
    "\n",
    "    regressor = DecisionTreeRegressor()\n",
    "    params = {'max_depth': range(1,11)}\n",
    "    scoring_fnc = make_scorer(performance_metric)\n",
    "    grid = GridSearchCV(regressor,params,scoring=scoring_fnc,cv=cv_sets)\n",
    "    grid = grid.fit(X, y)\n",
    "\n",
    "    # Return the optimal model after fitting the data\n",
    "    return grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions\n",
    "Once a model has been trained on a given set of data, it can be used to make predictions on new sets of input data. In the case of a *decision tree regressor*, the model has learned *what the best questions to ask about the input data are*, and can respond with a prediction for the **target variable**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimal Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 'max_depth' is 4 for the optimal model.\n"
     ]
    }
   ],
   "source": [
    "# Fit the training data to the model using grid search\n",
    "reg = fit_model(X_train, y_train)\n",
    "\n",
    "# Produce the value for 'max_depth'\n",
    "print(\"Parameter 'max_depth' is {} for the optimal model.\".format(reg.get_params()['max_depth']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** The optimal model has a max depth of 4, which is close enough to the previous guess of 3 earlier. Going back to the complexity graph, the model might perform better on the test set with a max depth of 4, but since the difference is minimal (which can also be due to noise), it might make sense to chose the simpler model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting Selling Prices\n",
    "\n",
    "Imagine that you were a real estate agent in the Boston area looking to use this model to help price homes owned by your clients that they wish to sell. You have collected the following information from three of your clients:\n",
    "\n",
    "| Feature | Client 1 | Client 2 | Client 3 |\n",
    "| :---: | :---: | :---: | :---: |\n",
    "| Total number of rooms in home | 5 rooms | 4 rooms | 8 rooms |\n",
    "| Neighborhood poverty level (as %) | 17% | 32% | 3% |\n",
    "| Student-teacher ratio of nearby schools | 15-to-1 | 22-to-1 | 12-to-1 |\n",
    "\n",
    "Let's see what prices our model will predict for these clients, and if they seem reasonable given the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted selling price for Client 1's home: $401,333.33\n",
      "Predicted selling price for Client 2's home: $240,947.37\n",
      "Predicted selling price for Client 3's home: $893,700.00\n"
     ]
    }
   ],
   "source": [
    "# Produce a matrix for client data\n",
    "client_data = [[5, 17, 15], # Client 1\n",
    "               [4, 32, 22], # Client 2\n",
    "               [8, 3, 12]]  # Client 3\n",
    "\n",
    "# Show predictions\n",
    "for i, price in enumerate(reg.predict(client_data)):\n",
    "    print(\"Predicted selling price for Client {}'s home: ${:,.2f}\".format(i+1, price))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prices seem reasonable given the features of the homes. We can see that the highest priced home has the most rooms, lowest neighbourhood poverty level, and the lowest student-teacher ratio, all of which make intuitive sense as discussed in the Question 1. On the contrary, the home with the lowest number of rooms, highest neighbourhood poverty level, and highest student-teacher ratio is priced the lowest in our predictions.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This finishes our project. We have built a decision tree regressor that performs reasonably well given the 3 features. But should the model should be used in a real-world setting? It probably shouldn't because: \n",
    "\n",
    "- The data collected in 1978 is not really relevant today due to rising population levels and changing population density of different areas. \n",
    "- The features present in the data that we built our model on are not likely be sufficient to describe a home. Examples of interesting features to look at may be *proximity to city center*, or *neighbourhood crime rate*.  \n",
    "- The data collected in an urban city will not be applicable in a rural city, because the people might value different aspects of a home depending on whether they live in an urban city or a rural area. For example, a person living in a rural city might value number of rooms in a home over the proximity to the city."
   ]
  }
 ],
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